LynchRisk: a Bayesian network to estimate the probability of Lynch syndrome based on pedigree and tumor characteristics
- 1LPSM - Sorbonne Universite
Abstract
BackgroundCurrent Lynch syndrome (LS) prediction models do not account for detailed family structure and tumour mismatch repair (MMR) status. We developed LynchRisk, a Bayesian model that estimates individualized LS probability from pedigree, cancer history and tumour MMR test results.
MethodsLynchRisk combines Mendelian inheritance, age- and sex-specific cancer hazards, affected and unaffected relatives, and detailed MMR status. Parameters were derived from the literature.
ResultsIn two simulated cases of probands with LS-associated cancers, family structure and MMR status markedly changed LS probability estimates, with implications for germline testing decisions. LynchRisk also identified situations in which a negative germline result might be considered sufficient to rule out LS or, conversely, prompt further investigations such as somatic MMR or long-read sequencing.
DiscussionLynchRisk gives individualized LS probability by combining clinical, family, and tumour data at different stages of the diagnostic process. Further work will focus on recalibrating cancer hazard functions, validating model performance through simulations, and developing an R package for use by interested research groups.
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BibTeX
@misc{lefebvre2026lynchrisk,
title = {{LynchRisk: a Bayesian network to estimate the probability of Lynch syndrome based on pedigree and tumor characteristics}},
author = {A. Lefebvre and P. Benusiglio and G. Nuel},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.14.26365520},
url = {https://www.medrxiv.org/content/10.64898/2026.10.14.26365520v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - LynchRisk: a Bayesian network to estimate the probability of Lynch syndrome based on pedigree and tumor characteristics
AU - A. Lefebvre
AU - P. Benusiglio
AU - G. Nuel
PY - 2026
DA - 2026/10/14
PB - medRxiv
DO - 10.64898/2026.10.14.26365520
UR - https://www.medrxiv.org/content/10.64898/2026.10.14.26365520v1
AB - BackgroundCurrent Lynch syndrome (LS) prediction models do not account for detailed family structure and tumour mismatch repair (MMR) status. We developed LynchRisk, a Bayesian model that estimates individualized LS probability from pedigree, cancer history and tumour MMR test results. MethodsLynchRisk combines Mendelian inheritance, age- and sex-specific cancer hazards, affected and unaffected relatives, and detailed MMR status. Parameters were derived from the literature. ResultsIn two simulated cases of probands with LS-associated cancers, family structure and MMR status markedly changed LS probability estimates, with implications for germline testing decisions. LynchRisk also identified situations in which a negative germline result might be considered sufficient to rule out LS or, conversely, prompt further investigations such as somatic MMR or long-read sequencing. DiscussionLynchRisk gives individualized LS probability by combining clinical, family, and tumour data at different stages of the diagnostic process. Further work will focus on recalibrating cancer hazard functions, validating model performance through simulations, and developing an R package for use by interested research groups.
ER -